Personalysis and Strength Finder Analytics for the PSJH Clinical Analytics Team
Update: 22 April, 2021 (T. French)
Personalysis Profiles
Respondents: Â (n=44)
Amir A
Andrew B
Angelique R
Ari R
Ashley S
Bradley R
Caleb S
Chris K
Chuck L
Devendra M
Firouzeh M
Ford P
Funi C
Gino C
Jacob L
Jacob V
Jason M
Jessica Q
Jessica S
Jodi B
John M
Jonathn P
Kevin D
Keyana A
Laurel K
Linda D
Lizbeth M
Mauli M
Ngoc S
Pete F
Rocky R
Said S
Sarah N
Steve T
Susan H
Susan P
Thomas F
Thomas G
Tony R
Troy H
Vandana K
Vasyl A
Vickie H
Victoria A
Others
Personalysis Clustering
Clustering was used as a simple unsupervised learning technique seeking to cluster caregivers into homogeneous or similar subgroups (4) based on a combination of Personalysis dimension and color response scores.
Expedite (Red)
Contribution
Connection
Commitment
Explore (Blue)
Contribution
Connection
Commitment
Personalysis Teams
Below are 3 dimensional team views of all CA care givers with specific individuals called out for selected team. All care givers are clustered as well.
Adv. Analytics
Expedite (Red)
Clusters:
Explore (Blue)
Clusters:
Organize (Green)
Clusters:
Collaborate (Yellow)
Clusters:
Executives
Expedite (Red)
Clusters:
Explore (Blue)
Clusters:
Organize (Green)
Clusters:
Collaborate (Yellow)
Clusters:
CaOps, Data Eng, Adv Analytics
Expedite (Red)
Clusters:
Explore (Blue)
Clusters:
Organize (Green)
Clusters:
Collaborate (Yellow)
Clusters:
Clifton Strength Finder
var variables =""for (var name inthis) variables += name +"\n";console.log(variables)/*This could work too... but it's such a big unecessary code for something you could do in one line var split = variables.split("\n");for (var i in split) console.log(split[i])*/